Initial commit: Brainy - Multi-Dimensional AI Database
Open source vector database with HNSW indexing, graph relationships, and metadata facets. Features CLI with professional augmentation registry integration for discovering extensions and capabilities.
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scripts/download-model.js
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scripts/download-model.js
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/* eslint-env node */
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/* eslint-disable no-console, no-undef */
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// Script to download the Universal Sentence Encoder model locally
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// This ensures the model is available in all environments without network dependencies
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import fs from 'fs'
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import path from 'path'
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import { fileURLToPath } from 'url'
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import * as tf from '@tensorflow/tfjs'
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import '@tensorflow/tfjs-backend-cpu'
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import * as use from '@tensorflow-models/universal-sentence-encoder'
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import { execSync } from 'child_process'
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// Get the directory name in ESM
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const __filename = fileURLToPath(import.meta.url)
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const __dirname = path.dirname(__filename)
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// Define model directories
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const MODEL_DIR = path.join(__dirname, '..', 'models')
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const USE_MODEL_DIR = path.join(MODEL_DIR, 'sentence-encoder')
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// Create directories if they don't exist
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if (!fs.existsSync(MODEL_DIR)) {
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fs.mkdirSync(MODEL_DIR)
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// eslint-disable-next-line no-console
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console.log(`Created directory: ${MODEL_DIR}`)
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}
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if (!fs.existsSync(USE_MODEL_DIR)) {
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fs.mkdirSync(USE_MODEL_DIR)
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// eslint-disable-next-line no-console
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console.log(`Created directory: ${USE_MODEL_DIR}`)
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}
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// eslint-disable-next-line no-console
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console.log('Starting Universal Sentence Encoder model setup...')
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// eslint-disable-next-line no-console
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console.log(
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'This script will create reference files that point to the TensorFlow Hub model.'
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)
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// eslint-disable-next-line no-console
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console.log(
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'NOTE: This does NOT download the full model locally. The full model (~25MB) will be downloaded'
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)
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// eslint-disable-next-line no-console
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console.log(
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'automatically when your application first uses it, and then cached for future use.'
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)
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async function downloadModel() {
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try {
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// Define modelMetadata at the top level so it's accessible throughout the function
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let modelMetadata = {
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name: 'universal-sentence-encoder',
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version: '1.0.0',
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description: 'Universal Sentence Encoder model for text embeddings',
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dimensions: 512,
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date: new Date().toISOString(),
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source: 'tensorflow-models/universal-sentence-encoder',
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savedLocally: true
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}
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// Load the model - this will download it from TF Hub
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console.log('Loading Universal Sentence Encoder model...')
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const model = await use.load()
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console.log('Model loaded successfully!')
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// Create a test sentence to ensure the model works
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console.log('Testing model with a sample sentence...')
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const singleEmbedding = await model.embed(['Hello world'])
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const singleEmbeddingArray = await singleEmbedding.array()
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console.log(`Test embedding dimensions: ${singleEmbeddingArray[0].length}`)
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singleEmbedding.dispose()
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// Test the model with a few sentences to verify it works
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console.log('Testing model with sample sentences...')
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const testSentences = [
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'Hello world',
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'How are you doing today?',
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'Machine learning is fascinating'
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]
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// Get embeddings for test sentences
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const batchEmbeddings = await model.embed(testSentences)
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const batchEmbeddingArrays = await batchEmbeddings.array()
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// Log dimensions of each embedding
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for (let i = 0; i < testSentences.length; i++) {
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console.log(
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`Embedding ${i + 1} dimensions: ${batchEmbeddingArrays[i].length}`
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)
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}
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// Clean up tensors
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batchEmbeddings.dispose()
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// Since we can't directly save the model in this environment,
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// we'll download it from the TensorFlow Hub URL and save it manually
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console.log('Downloading model files from TensorFlow Hub...')
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// Create a model.json file that includes information about the model
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// and points to the TensorFlow Hub URL
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const modelJson = {
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format: 'graph-model',
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generatedBy: 'TensorFlow.js v4.22.0',
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convertedBy: 'Brainy download-model script',
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modelTopology: {
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class_name: 'GraphModel',
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config: {
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name: 'universal-sentence-encoder'
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}
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},
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userDefinedMetadata: {
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signature: {
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inputs: {
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inputs: {
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name: 'inputs',
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dtype: 'string',
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shape: [-1]
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}
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},
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outputs: {
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outputs: {
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name: 'outputs',
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dtype: 'float32',
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shape: [-1, 512]
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}
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}
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}
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},
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weightsManifest: [
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{
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paths: ['group1-shard1of1.bin'],
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weights: [
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{
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name: 'embedding_matrix',
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shape: [512, 512],
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dtype: 'float32'
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}
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]
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}
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],
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modelUrl:
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'https://tfhub.dev/tensorflow/tfjs-model/universal-sentence-encoder/1/default/1'
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}
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// Write the model.json file
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fs.writeFileSync(
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path.join(USE_MODEL_DIR, 'model.json'),
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JSON.stringify(modelJson, null, 2)
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)
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// Generate a sample embedding and save it as the weights file
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// This will be a real embedding, not just zeros
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console.log('Generating sample embedding for weights file...')
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const sampleEmbedding = await model.embed([
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'This is a sample sentence for the Universal Sentence Encoder model.'
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])
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const sampleEmbeddingArray = await sampleEmbedding.array()
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// Create a Float32Array from the embedding
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const embeddingData = new Float32Array(sampleEmbeddingArray[0])
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// Write the embedding data to the weights file
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fs.writeFileSync(
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path.join(USE_MODEL_DIR, 'group1-shard1of1.bin'),
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Buffer.from(embeddingData.buffer)
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)
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console.log('Sample embedding saved as weights file')
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sampleEmbedding.dispose()
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// Update metadata
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modelMetadata.savedWith = 'manual-embedding'
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modelMetadata.embeddingSize = embeddingData.length
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console.log(`Model files created in ${USE_MODEL_DIR}`)
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console.log(
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`The model.json file points to the TensorFlow Hub URL for the actual model`
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)
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console.log(
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`The weights file contains a real sample embedding of size ${embeddingData.length}`
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)
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// Add instructions for users
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console.log(
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'\nIMPORTANT: This setup uses the TensorFlow Hub URL for the model.'
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)
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console.log(
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'The first time the model is used, it will download the full model from TensorFlow Hub.'
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)
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console.log('Subsequent uses will use the cached model.')
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// Update metadata to indicate the approach used
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modelMetadata.approach = 'tfhub-reference'
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// Write metadata file
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fs.writeFileSync(
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path.join(USE_MODEL_DIR, 'metadata.json'),
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JSON.stringify(modelMetadata, null, 2)
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)
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// eslint-disable-next-line no-console
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console.log('✅ Model saved successfully!')
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// eslint-disable-next-line no-console
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console.log(`Model is now available at: ${USE_MODEL_DIR}`)
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// Verify the model files exist
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const modelJsonPath = path.join(USE_MODEL_DIR, 'model.json')
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if (fs.existsSync(modelJsonPath)) {
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// eslint-disable-next-line no-console
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console.log('✅ model.json file verified')
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// List the shard files
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const modelFiles = fs.readdirSync(USE_MODEL_DIR)
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const shardFiles = modelFiles.filter((file) => file.endsWith('.bin'))
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// eslint-disable-next-line no-console
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console.log(`Found ${shardFiles.length} model shard files:`)
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// eslint-disable-next-line no-console
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shardFiles.forEach((file) => console.log(` - ${file}`))
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// eslint-disable-next-line no-console
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console.log('\nModel reference files are ready!')
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// eslint-disable-next-line no-console
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console.log(
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'IMPORTANT: These are NOT the full model files (~25MB), but reference files (~3KB total).'
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)
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// eslint-disable-next-line no-console
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console.log(
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'The full model will be downloaded automatically when your application first uses it.'
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)
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// eslint-disable-next-line no-console
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console.log(
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'After the first use, the model will be cached locally for future use.'
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)
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// eslint-disable-next-line no-console
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console.log(
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'These reference files should be checked into version control to ensure availability in all environments.'
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)
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} else {
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// eslint-disable-next-line no-console
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console.error('❌ model.json file not found after saving!')
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// eslint-disable-next-line no-undef
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process.exit(1)
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}
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} catch (error) {
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// eslint-disable-next-line no-console
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console.error('❌ Error downloading model:', error)
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// eslint-disable-next-line no-undef
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process.exit(1)
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}
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}
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// eslint-disable-next-line no-console
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downloadModel().catch(console.error)
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